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Re: st: RE: Marginal effects after ivprobit


From   Tinna <statalist@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: Marginal effects after ivprobit
Date   Tue, 13 Sep 2005 17:56:39 -0400

And one more semi-related thing:

When I use the endogenous variable in it's continuous form (and a
square form) I get endless iterations until I give up. See below:


. ivprobit   employment (centbmi centbmisq=diet1 diet2)  centage
centagesq edu2 edu3 edu4 edu5 edu6 marriag
> e2 marriage3 marriage4 children

Fitting exogenous probit model

Iteration 0:   log likelihood = -597.05914
Iteration 1:   log likelihood = -415.52928
Iteration 2:   log likelihood =  -402.8589
Iteration 3:   log likelihood = -402.40821
Iteration 4:   log likelihood = -402.40667
Iteration 5:   log likelihood = -402.40667

Fitting full model

Iteration 0:   log likelihood = -9748.1502  (not concave)
Iteration 1:   log likelihood = -9582.4934  (not concave)
Iteration 2:   log likelihood = -9553.7582  (not concave)
Iteration 3:   log likelihood = -9547.7161  (not concave)
Iteration 4:   log likelihood = -9537.2101  (not concave)
Iteration 5:   log likelihood = -9526.3101  (not concave)
Iteration 6:   log likelihood =  -9525.619  (not concave)
Iteration 7:   log likelihood = -9524.4498  (not concave)
Iteration 8:   log likelihood = -9522.9543  (not concave)
Iteration 9:   log likelihood = -9518.4009  (not concave)
Iteration 10:  log likelihood = -9517.5952  (not concave)
Iteration 11:  log likelihood = -9516.7487  (not concave)
Iteration 12:  log likelihood =  -9512.821  (not concave)
Iteration 13:  log likelihood = -9512.2921  (not concave)
Iteration 14:  log likelihood = -9512.1517  (not concave)
Iteration 15:  log likelihood = -9511.7616  (not concave)
Iteration 16:  log likelihood = -9500.6588  (not concave)
Iteration 17:  log likelihood = -9499.9541  (not concave)
Iteration 18:  log likelihood =  -9477.155  (not concave)
Iteration 19:  log likelihood = -9475.7283  (not concave)
Iteration 20:  log likelihood = -9475.0241  (not concave)
Iteration 21:  log likelihood = -9396.2923  (not concave)
Iteration 22:  log likelihood = -9396.2152  (not concave)
Iteration 23:  log likelihood = -9393.6135  (not concave)
Iteration 24:  log likelihood = -9393.4571  (not concave)
Iteration 25:  log likelihood = -9383.8382  (not concave)
Iteration 26:  log likelihood = -9382.7582  (not concave)
Iteration 27:  log likelihood = -9380.8916  (not concave)
Iteration 28:  log likelihood =  -9380.044  (not concave)
Iteration 29:  log likelihood = -9378.4173  (not concave)
Iteration 30:  log likelihood = -9376.8372  (not concave)
Iteration 31:  log likelihood = -9376.0801  (not concave)
Iteration 32:  log likelihood = -9375.4403  (not concave)
Iteration 33:  log likelihood = -9374.4594  (not concave)
Iteration 34:  log likelihood = -9374.4547  (not concave)
Iteration 35:  log likelihood =  -9372.391  (not concave)
Iteration 36:  log likelihood =  -9371.909  (not concave)
Iteration 37:  log likelihood = -9364.3699  (not concave)
Iteration 38:  log likelihood = -9363.6614  (not concave)
Iteration 39:  log likelihood = -9363.2954  (not concave)
Iteration 40:  log likelihood = -9363.2702  (not concave)
Iteration 41:  log likelihood = -9362.4889  (not concave)
Iteration 42:  log likelihood = -9357.4852  (not concave)
Iteration 43:  log likelihood = -9356.6759  (not concave)
Iteration 44:  log likelihood = -9353.7967  (not concave)
Iteration 45:  log likelihood = -9353.6053  (not concave)
Iteration 46:  log likelihood = -9353.4411  (not concave)
Iteration 47:  log likelihood =  -9350.254  (not concave)
Iteration 48:  log likelihood = -9342.8995  (not concave)
Iteration 49:  log likelihood = -9341.0832  (not concave)
Iteration 50:  log likelihood =  -9340.873  (not concave)
Iteration 51:  log likelihood = -9329.6951  (not concave)
Iteration 52:  log likelihood =  -9329.658  (not concave)
Iteration 53:  log likelihood = -9329.1022  (not concave)
Iteration 54:  log likelihood = -9328.9638  (not concave)
Iteration 55:  log likelihood = -9328.8256  (not concave)
Iteration 56:  log likelihood = -9328.0734  
Iteration 57:  log likelihood = -9327.8131  (not concave)
Iteration 58:  log likelihood = -9327.6051  (not concave)
Iteration 59:  log likelihood = -9327.5932  
Iteration 60:  log likelihood = -9327.3608  (not concave)
Iteration 61:  log likelihood = -9327.3484  (not concave)
Iteration 62:  log likelihood = -9327.3454  (not concave)
Iteration 63:  log likelihood = -9327.3438  (not concave)
Iteration 64:  log likelihood = -9327.3425  (not concave)
Iteration 65:  log likelihood = -9327.3417  
Iteration 66:  log likelihood = -9327.3415  (not concave)
Iteration 67:  log likelihood =   -9327.34  (not concave)
Iteration 68:  log likelihood = -9327.3396  (not concave)
Iteration 69:  log likelihood = -9327.3393  
Iteration 70:  log likelihood = -9327.3378  (not concave)
Iteration 71:  log likelihood = -9327.3378  (not concave)
Iteration 72:  log likelihood = -9327.3378  (not concave)
Iteration 73:  log likelihood = -9327.3378  
Iteration 74:  log likelihood = -9327.3375  (not concave)
Iteration 75:  log likelihood = -9327.3374  (not concave)
Iteration 76:  log likelihood = -9327.3374  
Iteration 77:  log likelihood =  -9327.337  
Iteration 78:  log likelihood = -9327.3368  (not concave)
Iteration 79:  log likelihood = -9327.3364  
Iteration 80:  log likelihood = -9327.3359  (not concave)
Iteration 81:  log likelihood = -9327.3359  
Iteration 82:  log likelihood = -9327.3355  (not concave)
Iteration 83:  log likelihood = -9327.3354  
Iteration 84:  log likelihood = -9327.3351  
Iteration 85:  log likelihood = -9327.3348  (not concave)
Iteration 86:  log likelihood = -9327.3348  (not concave)
Iteration 87:  log likelihood = -9327.3347  (not concave)
Iteration 88:  log likelihood = -9327.3347  
Iteration 89:  log likelihood = -9327.3347  
Iteration 90:  log likelihood = -9327.3343  (not concave)
Iteration 91:  log likelihood = -9327.3343  (not concave)
Iteration 92:  log likelihood = -9327.3343  
Iteration 93:  log likelihood = -9327.3341  (not concave)
Iteration 94:  log likelihood = -9327.3341  (not concave)
Iteration 95:  log likelihood =  -9327.334  
--Break--
r(1);


On 9/13/05, Tinna <statalist@gmail.com> wrote:
> Hmmmm!
> 
> I appreciate your time guys.
> 
> Here are my commands and results.
> 
> . ivprobit   employment ( OBESEbmi=diet1 diet2)  centage centagesq
> edu2 edu3 edu4 edu5 edu6 marriage2 marri
> > age3 marriage4 children
> 
> 
> Fitting exogenous probit model
> 
> Iteration 0:   log likelihood = -597.05914
> Iteration 1:   log likelihood = -415.87674
> Iteration 2:   log likelihood = -403.33373
> Iteration 3:   log likelihood = -402.89898
> Iteration 4:   log likelihood = -402.89762
> Iteration 5:   log likelihood = -402.89762
> 
> Fitting full model
> 
> Iteration 0:   log likelihood = -746.36499
> Iteration 1:   log likelihood = -744.94761
> Iteration 2:   log likelihood = -744.38178
> Iteration 3:   log likelihood =    -744.38
> Iteration 4:   log likelihood =    -744.38
> 
> Probit model with endogenous regressors           Number of obs   =       1045
>                                                  Wald chi2(12)   =     281.90
> Log likelihood =    -744.38                       Prob > chi2     =     0.0000
> 
> ------------------------------------------------------------------------------
>             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> employment   |
>    OBESEbmi |  -.8835098   .4013275    -2.20   0.028    -1.670097   -.0969223
>     centage |  -.0114935   .0039625    -2.90   0.004    -.0192598   -.0037272
>   centagesq |  -.0026056   .0002318   -11.24   0.000    -.0030599   -.0021513
>        edu2 |  -.5586423   .1567106    -3.56   0.000    -.8657895   -.2514951
>        edu3 |    .152094   .1354019     1.12   0.261    -.1132888    .4174769
>        edu4 |   .4574337   .1725559     2.65   0.008     .1192304     .795637
>        edu5 |   .4399531   .1929985     2.28   0.023     .0616829    .8182233
>        edu6 |   .9912227   .3626966     2.73   0.006     .2803504    1.702095
>   marriage2 |  -.0014036    .181648    -0.01   0.994    -.3574272    .3546199
>   marriage3 |  -.4220406   .1645533    -2.56   0.010    -.7445591    -.099522
>   marriage4 |  -.6121703   .2256473    -2.71   0.007    -1.054431   -.1699096
>    children |  -.0192385   .0425978    -0.45   0.652    -.1027286    .0642516
>       _cons |   1.534364   .1681024     9.13   0.000      1.20489    1.863839
> -------------+----------------------------------------------------------------
>    /lnsigma |  -1.090328   .0218741   -49.85   0.000    -1.133201   -1.047456
>     /athrho |   .3088546   .1551784     1.99   0.047     .0047107    .6129986
> -------------+----------------------------------------------------------------
>       sigma |   .3361062    .007352                       .322001    .3508292
>         rho |   .2993948   .1412686                      .0047106    .5462345
> ------------------------------------------------------------------------------
> Instrumented:  OBESEbmi
> Instruments:   centage centagesq edu2 edu3 edu4 edu5 edu6 marriage2 marriage3
>               marriage4 children diet1 diet2
> ------------------------------------------------------------------------------
> Wald test of exogeneity (/athrho = 0): chi2(1) =     3.96 Prob > chi2 = 0.0466
> 
> . mfx
> 
> warning: predict() expression  unsuitable for standard-error calculation;
> option nose imposed
> 
> 
> Marginal effects after ivprobit
>      y  = Fitted values (predict)
>         =  .77898262
> -------------------------------------------------------------------------------
>                        variable |          dy/dx                 X
> ---------------------------------+---------------------------------------------
>                        OBESEbmi |       -.8835098            .159183
>                         centage |       -.0114935            -.21666
>                       centagesq |       -.0026056            251.145
>                            edu2 |       -.5586423            .125493
>                            edu3 |         .152094            .228086
>                            edu4 |        .4574337            .159307
>                            edu5 |        .4399531            .144449
>                            edu6 |        .9912227            .056753
>                       marriage2 |       -.0014036            .099244
>                       marriage3 |       -.4220406            .085597
>                       marriage4 |       -.6121703            .061284
>                        children |       -.0192385            2.40813
>                           diet1 |               0            .613461
>                           diet2 |               0            .150254
> -------------------------------------------------------------------------------
> 
> 
> On 9/13/05, Scott Merryman <smerryman@kc.rr.com> wrote:
> >
> > > -----Original Message-----
> > > From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-
> > > statalist@hsphsun2.harvard.edu] On Behalf Of Tinna
> > > Sent: Tuesday, September 13, 2005 3:37 PM
> > > To: statalist@hsphsun2.harvard.edu
> > > Subject: Re: st: RE: Marginal effects after ivprobit
> > >
> > > Thanks for the answer Scott.  Yes I am pretty sure.
> > >
> > > If you try the same estimations again without regressing quietly then
> > > you will probably see that the coefficients you get after mfx are the
> > > same as from the original estimation.
> >
> > <snip>
> >
> > But they are not.
> >
> > . webuse laborsup, clear
> >
> > . ivprobit fem_work fem_educ kids (other_inc = male_educ) , nolog
> >
> > Probit model with endogenous regressors        Number of obs   =        500
> >                                               Wald chi2(3)    =     163.88
> > Log likelihood = -2368.2062                    Prob > chi2     =     0.0000
> >
> > ----------------------------------------------------------------------------
> > Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
> > -------------+--------------------------------------------------------------
> > fem_work     |
> >   other_inc |  -.0542756   .0060854    -8.92   0.000    -.0662027   -.04234
> >    fem_educ |    .211111   .0268648     7.86   0.000     .1584569    .26376
> >        kids |  -.1820929   .0478267    -3.81   0.000    -.2758316   -.08835
> >       _cons |   .3672083   .4480724     0.82   0.412    -.5109975    1.2454
> > -------------+--------------------------------------------------------------
> >    /lnsigma |   2.813383   .0316228    88.97   0.000     2.751404    2.8753
> >     /athrho |   .3907858   .1509443     2.59   0.010     .0949403    .68663
> > -------------+--------------------------------------------------------------
> >       sigma |   16.66621   .5270318                      15.66461    17.731
> >         rho |   .3720374   .1300519                      .0946561    .59581
> > ----------------------------------------------------------------------------
> > Instrumented:  other_inc
> > Instruments:   fem_educ kids male_educ
> > ----------------------------------------------------------------------------
> > Wald test of exogeneity (/athrho = 0): chi2(1) =     6.70 Prob > chi2 =
> > 0.0096
> >
> > . estimates store iv
> >
> > . mfx, predict(p)
> >
> > warning: predict() expression p unsuitable for standard-error calculation;
> > option nose imposed
> >
> >
> > Marginal effects after ivprobit
> >      y  = Probability of positive outcome (predict, p)
> >         =  .44363395
> > ----------------------------------------------------------------------------
> >                        variable |          dy/dx                 X
> > ---------------------------------+------------------------------------------
> >                       other_inc |       -.0214364            49.6023
> >                        fem_educ |        .0833791             12.046
> >                            kids |       -.0719183              1.976
> >                       male_educ |               0             11.966
> > ----------------------------------------------------------------------------
> >
> > . estimates store mfx
> >
> > Or, all together for easy comparison:
> >
> > . estout iv mfx, style(fixed) margin meqs(fem_work) label varwidth(24)
> > varlabels(_cons Constant) keep(fem_work:) collabels(,none)
> >
> >                                   iv          mfx
> > Does female work?
> > Other income                -.0542756    -.0214364
> > Female education level        .211111     .0833791
> > Number of children          -.1820929    -.0719183
> > Constant
> >
> >
> > Scott
> >
> >
> >
> >
> > *
> > *   For searches and help try:
> > *   http://www.stata.com/support/faqs/res/findit.html
> > *   http://www.stata.com/support/statalist/faq
> > *   http://www.ats.ucla.edu/stat/stata/
> >
>

*
*   For searches and help try:
*   http://www.stata.com/support/faqs/res/findit.html
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